Clinical simulation fidelity and nurses' identification of critical event risk: a signal detection analysis

Carl Thompson*, Huiqin Yang, Simon Crouch

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Aims. This article is a report of a study exploring the effect of increasing fidelity on nurses risk detection in clinical simulation and the effect of clinical experience on nurses risk detection ability. Background. Clinical environments can be recreated successfully using simulation. However, how judgement changes as simulation fidelity increases is unknown. Knowledge of the effects of increased fidelity on judgement may help in the design of educational interventions seeking to improve clinical judgement in nurses. Design. Quasi experimental signal detection study. Method. During 20082009, using a quasi experimental signal detection design, 63 nursing students and 34 experienced nurses were presented with 25 paper and 25 human simulator cases based on real patient records from a single UK National Health Service hospital. Nurses judged whether a simulated case was at risk or not at risk of a critical event. Clinical judgement performance was measured using standard signal detection measures. Findings. Judgement performance, as measured by hit rates and signal detection ability were significantly lower in higher fidelity clinical simulations. False alarm rates and bias (beta) did not differ according to the fidelity of simulation. Clinical experience did not predict the ability to detect risk. Conclusion. As fidelity of simulation increased, both novice and experienced nurses were less likely to be able to separate important clinical risk from clinical noise in a simulated clinical environment. (c) 2012 Blackwell Publishing Ltd

Original languageEnglish
Pages (from-to)2477-2485
Number of pages9
JournalJournal of Advanced Nursing
Issue number11
Early online date7 Feb 2012
Publication statusPublished - Nov 2012


  • nurses
  • clinical decision-making
  • nursing judgement
  • signal detection
  • critical event risk
  • nursing
  • clinical simulation

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